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LangGraph Platform quickstart

This guide shows you how to run a LangGraph application locally.

Prerequisites

Before you begin, ensure you have the following:

This quickstart uses the react-agent template and requires the following:

1. Install the LangGraph CLI

# Python >= 3.11 is required.

pip install --upgrade "langgraph-cli[inmem]"

2. Create a LangGraph app 🌱

Create a new app from the react-agent template. This template is a simple agent that can be flexibly extended to many tools.

langgraph new path/to/your/app --template react-agent-python 

langgraph new path/to/your/app --template react-agent-js

Additional templates

If you use langgraph new without specifying a template, you will be presented with an interactive menu that will allow you to choose from a list of available templates.

3. Install dependencies

In the root of your new LangGraph app, install the dependencies in edit mode so your local changes are used by the server:

cd path/to/your/app
pip install -e .

cd path/to/your/app
yarn install

4. Create a .env file

You will find a .env.example in the root of your new LangGraph app. Create a .env file in the root of your new LangGraph app and copy the contents of the .env.example file into it, filling in the necessary API keys:

LANGSMITH_API_KEY=lsv2...
TAVILY_API_KEY=tvly-...
ANTHROPIC_API_KEY=sk-
OPENAI_API_KEY=sk-...

5. Launch LangGraph Server 🚀

Start the LangGraph API server locally:

langgraph dev

npx @langchain/langgraph-cli dev

Sample output:

>    Ready!
> 
>    - API: [http://localhost:2024](http://localhost:2024/)
>     
>    - Docs: http://localhost:2024/docs
>     
>    - LangGraph Studio Web UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024

The langgraph dev command starts LangGraph Server in an in-memory mode. This mode is suitable for development and testing purposes. For production use, deploy LangGraph Server with access to a persistent storage backend. For more information, see Deployment options.

6. Test your application in LangGraph Studio

LangGraph Studio is a specialized UI that you can connect to LangGraph API server to visualize, interact with, and debug your application locally. Test your graph in LangGraph Studio by visiting the URL provided in the output of the langgraph dev command:

>    - LangGraph Studio Web UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024

For a LangGraph Server running on a custom host/port, update the baseURL parameter.

Safari compatibility

Use the --tunnel flag with your command to create a secure tunnel, as Safari has limitations when connecting to localhost servers:

langgraph dev --tunnel

7. Test the API

  1. Install the LangGraph Python SDK:

    pip install langgraph-sdk

  2. Send a message to the assistant (threadless run):

    from langgraph_sdk import get_client
import asyncio

client = get_client(url="http://localhost:2024")

async def main():
    async for chunk in client.runs.stream(
        None,  # Threadless run
        "agent", # Name of assistant. Defined in langgraph.json.
        input={
        "messages": [{
            "role": "human",
            "content": "What is LangGraph?",
            }],
        },
    ):
        print(f"Receiving new event of type: {chunk.event}...")
        print(chunk.data)
        print("\n\n")

asyncio.run(main())

  1. Install the LangGraph Python SDK:

    pip install langgraph-sdk

  2. Send a message to the assistant (threadless run):

    from langgraph_sdk import get_sync_client

client = get_sync_client(url="http://localhost:2024")

for chunk in client.runs.stream(
    None,  # Threadless run
    "agent", # Name of assistant. Defined in langgraph.json.
    input={
        "messages": [{
            "role": "human",
            "content": "What is LangGraph?",
        }],
    },
    stream_mode="messages-tuple",
):
    print(f"Receiving new event of type: {chunk.event}...")
    print(chunk.data)
    print("\n\n")

  1. Install the LangGraph JS SDK:

    npm install @langchain/langgraph-sdk

  2. Send a message to the assistant (threadless run):

    const { Client } = await import("@langchain/langgraph-sdk");

// only set the apiUrl if you changed the default port when calling langgraph dev
const client = new Client({ apiUrl: "http://localhost:2024"});

const streamResponse = client.runs.stream(
    null, // Threadless run
    "agent", // Assistant ID
    {
        input: {
            "messages": [
                { "role": "user", "content": "What is LangGraph?"}
            ]
        },
        streamMode: "messages-tuple",
    }
);

for await (const chunk of streamResponse) {
    console.log(`Receiving new event of type: ${chunk.event}...`);
    console.log(JSON.stringify(chunk.data));
    console.log("\n\n");
}

curl -s --request POST \
    --url "http://localhost:2024/runs/stream" \
    --header 'Content-Type: application/json' \
    --data "{
        \"assistant_id\": \"agent\",
        \"input\": {
            \"messages\": [
                {
                    \"role\": \"human\",
                    \"content\": \"What is LangGraph?\"
                }
            ]
        },
        \"stream_mode\": \"messages-tuple\"
    }"

Next Steps

Now that you have a LangGraph app running locally, take your journey further by exploring deployment and advanced features: